{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/69ab3b7c7036d739021982df/6a7ca71891cf66ecf8a3f342?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"AI Agent Context Files: How to Steer Long Projects","description":"<p>For deeper playbooks and analysis: <a href=\"https://natesnewsletter.substack.com/\" rel=\"noopener noreferrer\" target=\"_blank\">https://natesnewsletter.substack.com/</a></p><p><br></p><p>What's really happening when an AI agent has access to more context than it can use well?</p><p>The common story is that better AI work requires preserving everything — but the reality is that current human judgment needs to remain in charge.</p><p>In this video, I share the inside scoop on progressive context shaping: how to separate stable instructions, current state, retrieval maps, and history so an agent can keep moving without stale decisions steering the work.</p><p><br></p><ul><li>Why giant instruction files become graveyards of stale rules</li><li>How a maintained current-state file keeps judgment fresh</li><li>What the four kinds of context are and where each belongs</li><li>Why focused context can outperform a full context window</li><li>How to design useful checkpoints that produce reviewable work</li></ul><p><br></p><p>For operators and builders managing long-running agent work, the goal is not perfect memory. It is a system that lets evidence update the plan before outdated judgment compounds.</p><p><br></p><p>Subscribe for daily AI strategy and news.</p>","author_name":"Nate B. Jones"}